I do not know about you, but when I have a lot of tasks on my mind and I find myself in the middle of writing comprehensive emails, I would definitely appreciate some help. ChatGPT3 has been used in a wide range of applications since 2020, so why not use for improving your daily life? One of the main benefits of ChatGPT3 is its ability to generate human-like text with a high degree of accuracy. This has the potential to transform a wide range of industries, including customer service, education, and creative writing. Your emails are a reflection of you and your professionalism.

OpenAI's Legal team plays an indispensable role in advancing OpenAI's mission by navigating futuristic, foundational legal issues in AI. This is the team for you if you care deeply about doing meaningful and novel work as a technology lawyer. The team comprises various backgrounds, including technology, venture capital, M&A, employment, and tax law. As an AI Product Counsel, you will support and lead product, privacy, and regulatory legal initiatives for our cutting-edge models and technologies. This is a unique opportunity to be directly involved in the forefront of the legal and technology field.

Github Co-Pilot is a cutting-edge AI-poGithubred tool developed by GitHub and OpenAI that offers an innovative way for developers to write code. Using advanced machine learning algorithms, Co-Pilot provides intelligent suggestions and auto-completion for code snippets, making it easier and faster for programmers to write complex programs. One of the critical advantages of Co-Pilot is its ability to learn and adapt to the coding patterns and styles of individual developers. As developers work with Co-Pilot, it becomes more familiar with their coding habits and can offer more personalized suggestions and assistance. Moreover, Co-Pilot is built on OpenAI's state-of-the-art GPT language model, which is trained on a massive corpus of data and can understand natural language queries and generate human-like responses.

ChatGPT, the AI content tool, is switching to a freemium model, with a new paid tier, priced at $20 per month, which will give users better access to the tool. A version of the ChatGPT interface will remain free to use, but you may be subject to delays at peak usage times and/or outages depending on circumstance.

Recently, ChatGPT has gained significant attention in research due to its ability to interact with humans effectively. The core idea behind this model is reinforcement learning (RL) fine-tuning, a new paradigm that allows language models to align with human preferences, i.e., InstructGPT. In this study, we propose BadGPT, the first backdoor attack against RL fine-tuning in language models. By injecting a backdoor into the reward model, the language model can be compromised during the fine-tuning stage. Our initial experiments on movie reviews, i.e., IMDB, demonstrate that an attacker can manipulate the generated text through BadGPT.

Prompt engineering is an increasingly important skill set needed to converse effectively with large language models (LLMs), such as ChatGPT. Prompts are instructions given to an LLM to enforce rules, automate processes, and ensure specific qualities (and quantities) of generated output. Prompts are also a form of programming that can customize the outputs and interactions with an LLM. This paper describes a catalog of prompt engineering techniques presented in pattern form that have been applied to solve common problems when conversing with LLMs. Prompt patterns are a knowledge transfer method analogous to software patterns since they provide reusable solutions to common problems faced in a particular context, i.e., output generation and interaction when working with LLMs. This paper provides the following contributions to research on prompt engineering that apply LLMs to automate software development tasks. First, it provides a framework for documenting patterns for structuring prompts to solve a range of problems so that they can be adapted to different domains. Second, it presents a catalog of patterns that have been applied successfully to improve the outputs of LLM conversations. Third, it explains how prompts can be built from multiple patterns and illustrates prompt patterns that benefit from combination with other prompt patterns.

Recent advances in the capacity of large language models to generate human-like text have resulted in their increased adoption in user-facing settings. In parallel, these improvements have prompted a heated discourse around the risks of societal harms they introduce, whether inadvertent or malicious. Several studies have explored these harms and called for their mitigation via development of safer, fairer models. Going beyond enumerating the risks of harms, this work provides a survey of practical methods for addressing potential threats and societal harms from language generation models. We draw on several prior works' taxonomies of language model risks to present a structured overview of strategies for detecting and ameliorating different kinds of risks/harms of language generators. Bridging diverse strands of research, this survey aims Figure 1: Overview of Intervention Strategies. A typical to serve as a practical guide for both LM researchers ML/NLP model development process involves data and practitioners, with explanations collection/curation, model training and design, inference, of different mitigation strategies' motivations, and finally application deployment.

Fine-tuning a language model on a new domain is standard practice for domain adaptation. However, it can be infeasible when it comes to modern large-scale language models such as GPT-3, which can only be accessed through APIs, making it difficult to access the internal parameters of the model. In this paper, we propose $k$NN-Adapter, a method to effectively adapt these black-box large language models (LLMs) to a new domain. The $k$NN-Adapter builds on top of the retrieval-augmented language model, and adaptively learns to interpolate the output of the language model with retrieval results from a datastore consisting of the target domain data. Our experiments on four different domains demonstrate that $k$NN-Adapter significantly improves perplexity, and works particularly well in settings with limited access to LLMs. Additionally, we show that $k$NN-Adapter is more effective than fine-tuning when the amount of training data is limited. We also release a dataset to encourage further study.

In recent years, there has been a growing interest in the development of language models capable of generating text with controllable attributes. While several approaches have been proposed, many of these methods require condition-specific data or significant computational resources. In this study, we propose a novel method called Gamma Sampling, which enables controllable language generation without the need for any training data and maintains a fast generation speed. Gamma Sampling incorporates attribute-related information into the sampling process, effectively guiding the language model to produce text with desired attributes. Our experimental results demonstrate that Gamma Sampling, when applied to GPT2, outperforms representative baselines in terms of diversity, attribute relevance, and overall quality of the generated samples.

The Werewolf game is a social deduction game based on free natural language communication, in which players try to deceive others in order to survive. An important feature of this game is that a large portion of the conversations are false information, and the behavior of artificial intelligence (AI) in such a situation has not been widely investigated. The purpose of this study is to develop an AI agent that can play Werewolf through natural language conversations. First, we collected game logs from 15 human players. Next, we fine-tuned a Transformer-based pretrained language model to construct a value network that can predict a posterior probability of winning a game at any given phase of the game and given a candidate for the next action. We then developed an AI agent that can interact with humans and choose the best voting target on the basis of its probability from the value network. Lastly, we evaluated the performance of the agent by having it actually play the game with human players. We found that our AI agent, Deep Wolf, could play Werewolf as competitively as average human players in a villager or a betrayer role, whereas Deep Wolf was inferior to human players in a werewolf or a seer role. These results suggest that current language models have the capability to suspect what others are saying, tell a lie, or detect lies in conversations.